The definition of conditional probability yields the following result,
- obtained by multiplying both sides of Equation (2.3) in defintion of conditional probability by .
The Multiplication Rule
- This rule is important because it is often the case that
- is desired,
- whereas both and can be obtained from the problem description.
- Consideration of gives
- The multiplication rule is most useful when
- the experiment consists of several stages in succession.
- The conditioning event then describes the outcome of the first stage
- and the outcome of the second,
- so that (conditioning on what occurs first) will often be known.
- The rule is easily extended to experiments involving more than two stages.
Example
Consider three events , , and .
- The triple intersection of these events can be represented as the double intersection .
- Applying our previous multiplication rule to this intersection
- and then to gives
\begin{align} &P\left( {{A}{1} \cap {A}{2} \cap {A}{3}}\right) \ = &P\left( {{A}{3} \mid {A}{1} \cap {A}{2}}\right) \cdot P\left( {{A}{1} \cap {A}{2}}\right) \ = &P\left( {{A}{3} \mid {A}{1} \cap {A}{2}}\right) \cdot P\left( {{A}{2} \mid {A}{1}}\right) \cdot P\left( {A}{1}\right) \end{align}
Remark
- When the experiment of interest consists of a sequence of several stages,
- it is convenient to represent these with a tree diagram.
- Once we have an appropriate tree diagram,
- probabilities and conditional probabilities can be entered on the various branches;
- this will make repeated use of the multiplication rule quite straightforward.